11 papers
WiFo-INR: A Wireless Foundation Model Based on Implicit Neural Representations
Boxun Liu, Xuanyu Liu, Shijian Gao +2
Wireless foundation models are emerging as a promising paradigm for AI-native physical-layer design. However, existing methods typically model channel state information (CSI) as im…
WiFo-2: a generalist foundation model unifies heterogeneous wireless system design
Boxun Liu, Xuanyu Liu, Shijian Gao +3
Emerging sixth-generation wireless systems are increasingly heterogeneous, with compatibility across diverse configurations, ubiquitous coverage, and expanded functionalities. Alth…
WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM)
Xuanyu Liu, Shijian Gao, Boxun Liu +2
Current learning-based wireless methods struggle with generalization due to the fragmented processing of communication and sensing data. WiFo-MiSAC addresses this as a task-agnosti…
Large Wireless Foundation Models: Stronger over Bigger
Xiang Cheng, Boxun Liu, Xuanyu Liu +1
AI-communication integration is widely regarded as a core enabling technology for 6G. Most existing AI-based physical-layer designs rely on task-specific models that are separately…
Embodied Intelligent Wireless (EIW): Synesthesia of Machines Empowered Wireless Communications
Xiang Cheng, Weibo Wen, Haotian Zhang +4
The evolution toward the sixth-generation (6G) and beyond mobile communication systems is marked by a fundamental shift from merely connecting devices to enabling pervasive and emb…
LLM4AMC: Adapting Large Language Models for Adaptive Modulation and Coding
Xinyu Pan, Boxun Liu, Xiang Cheng +1
Adaptive modulation and coding (AMC) is a key technology in 5G new radio (NR), enabling dynamic link adaptation by balancing transmission efficiency and reliability based on channe…